Spatial Analysis and Modeling of the Housing Value Changes in the U.S. during the COVID-19 Pandemic

Li, Xinba; Kao, Chihwa

doi:10.3390/jrfm15030139

Cited by 9 publications

(5 citation statements)

References 55 publications

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“…The spillover effect of Shanghai and Guangzhou in the YRDUA was not significant, due to the high proportion of cities that had a strong spillover effect of housing prices in the YRDUA. This is attributed to the fact that housing market fluctuations are amplified by the divergent distribution of regional socioeconomic factors [69], which will lead to uneven housing prices' spillover effects among cities. Among China's three largest urban agglomerations, housing prices are aggregating and the spatiotemporal heterogeneity of the housing prices' association is apparent and more evident than in previous studies of small urban regionals' interactions [15,22,27].…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Spatiotemporal Heterogeneity of Housing Prices’ Association in China: An Urban Agglomeration Perspective

et al. 2022

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With the rise of urban agglomerations, regional divergence of China’s real estate market has gradually intensified. City-specialized policies have become the main emphasis for promoting the healthy development of the regional real estate market. By adopting a gravity model, social net-work analysis, and impulse response analysis, this paper examines the spatial-temporal heterogeneity of housing prices’ association in the Beijing-Tianjin-Hebei Urban Agglomeration (BTH-UA), the Yangtze River Delta Urban Agglomeration (YRDUA), and the Pearl River Delta Urban Agglomeration (PRDUA), which are the most developed urban agglomerations in China. Meanwhile, the formation mechanism of the housing prices’ association network and spillover effect in urban agglomeration were theoretically analyzed. This paper found that (1) significant aggregation phenomena of housing prices were observed in the urban agglomerations; (2) characteristics of overall and individual networks were dynamically heterogeneous. In the BTHUA and the PRDUA, the associations of housing prices were polarized and sparse, while they were more linked and complex in the YRDUA; (3) polycentric network structure has been demonstrated in the urban agglomerations and the spillover effects of central cities varied in intensity and breadth on responding cities and persisted during the lag period. Accordingly, several policy recommendations have been made.

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Section: Discussionmentioning

confidence: 99%

Analysis of the Spatiotemporal Heterogeneity of Housing Prices’ Association in China: An Urban Agglomeration Perspective

et al. 2022

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“…Since GWR has been used for real-estate research, the non-stationary nature and spatial heterogeneity of housing prices have been widely revealed. Subsequently, an increasing number of studies have explored the non-stationary nature and spatial heterogeneity of housing prices from different perspectives [1][2][3][25][26][27][28][29][30][31][32]. For example, Yu et al have employed GWR to examine the spatial dependence and heterogeneity of housing-market dynamics in the city of Milwaukee [29].…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al have used geographically neural network weighted regression (GNNWR) to improve the accuracy of real estate evaluation [2]. Li et al have used the GWR model to investigate the spatial impacts of COVID-19 on housing price changes in the U.S. real estate market [32].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Spatial Discrete Heterogeneity of Housing Prices in Beijing, China, Based on Regionally Geographically Weighted Regression Affected by Education

Wang

Zhang

Zhao

2023

Land

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Spatial heterogeneity analysis of housing prices, in general, is crucial for maintaining high-quality economic development in China, especially in the post-COVID-19 pandemic context. Previous studies have attempted to explain the associated geographical evolution by studying the spatial non-stationary continuous heterogeneity; however, they ignored the spatial discrete heterogeneity caused by natural or policy factors, such as education, economy, and population. Therefore, in this study, we take Beijing as an example and consider educational factors in order to propose an improved local regression algorithm called the regionally geographically weighted regression affected by education (E-RGWR), which can effectively address spatial non-stationary discrete heterogeneity caused by education factors. Our empirical study indicates that the R2 and R2adj values of E-RGWR are 0.8644 and 0.8642, which are 10.98% and 11.01% higher than those of GWR, and 3.26% and 3.27% higher than those of RGWR, respectively. In addition, through an analysis of related variables, the quantitative impacts of greening rate, distance to market, distance to hospitals. and construction time on housing prices in Beijing are found to present significant spatial discrete heterogeneity, and a positive relationship between school districts and housing prices was also observed. The obtained evaluation results indicate that E-RGWR can explain the spatial instability of housing prices in Beijing and the spatial discrete heterogeneity caused by education factors. Finally, based on the estimation results of the E-RGWR model, regarding housing prices in Beijing, we analyze the relationships between enrollment policy, real estate sales policy, and housing prices, E-RGWR can provide policy makers with more refined evidence to understand the nature of the centralized change relationship of Beijing’s housing price data in a well-defined manner. The government should not only carry out macro-control, but also implement precise policies for different regions, refine social governance, promote education equity, and boost the economy.

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“…The global liquidity easing policy to prevent a recession due to the COVID-19 pandemic is causing a surge in asset values [1]. In response, the U.S. has aggressively raised interest rates, while South Korea has promoted large-scale housing supply by developing new cities in the third phase to address the problem of rapidly rising housing prices.…”

Section: Introductionmentioning

confidence: 99%

Predicting Commercial Land Requirements in New Cities using Big Data Analysis of Expenditure and Traffic Data

Kim,

Kim

2023

APJCRI

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In developing new cities in Korea, inaccurate land demand forecasts can cause social problems such as the stagnation of commercial areas and large-scale unsold commercial facilities. In particular, stagnation of commercial areas and large-scale unsold commercial facilities in new cities reduce the attractiveness of the entire commercial area of new cities, causing great damage to the selfemployed in the area. In Korea, more than 10 new cities were created from the 1980s to the early 2000s, but the stagnation of commercial space in the new city is considered a problem due to the prediction of demand for commercial land. This study aims to estimate the appropriate purchasing power of new cities based on the actual consumption expenditure and transportation demand of new cities, and to estimate the appropriate gross commercial area. The subjects of the study are five new cities in the Seoul metropolitan area, including Byeonae, Wirye, Misa, Okjeong, and Dasan. This study uses secondary data for analysis, including card usage data and traffic OD data for each new city. As a result of the analysis, the amount of consumption or purchasing power spent in the new cities was estimated to be around KRW 200 billion to KRW 300 billion. Considering the average annual sales per square meter of commercial facilities in Gyeonggi-do, the estimated total commercial area per new city was approximately 50,000 to 70,000 square meters. When compared with the actual total commercial area supplied to each new city, the difference ranged from 3.8 to 5.5 times. These findings have important implications for the oft-cited oversupply of commercial space in new cities and the resulting unsold and frequent closures. The results of this study will provide implications for the planning and supply of appropriate commercial land in subsequent development projects such as the third new city.

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Spatial Analysis and Modeling of the Housing Value Changes in the U.S. during the COVID-19 Pandemic

Cited by 9 publications

References 55 publications

Analysis of the Spatiotemporal Heterogeneity of Housing Prices’ Association in China: An Urban Agglomeration Perspective

Analysis of the Spatiotemporal Heterogeneity of Housing Prices’ Association in China: An Urban Agglomeration Perspective

Exploring the Spatial Discrete Heterogeneity of Housing Prices in Beijing, China, Based on Regionally Geographically Weighted Regression Affected by Education

Predicting Commercial Land Requirements in New Cities using Big Data Analysis of Expenditure and Traffic Data

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